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pro vyhledávání: '"Novara P"'
Nonlinear Model Predictive Control (NMPC) is a general and flexible control approach, used in many industrial contexts, and is based on the online solution of a nonlinear optimization problem. This operation requires in general a high computational c
Externí odkaz:
http://arxiv.org/abs/2410.19467
This paper presents a tool for multi-step system identification that leverages first-order optimization and exact gradient computation. Drawing inspiration from neural network training and Automatic Differentiation (AD), the proposed method computes
Externí odkaz:
http://arxiv.org/abs/2410.03544
Many scientific problems aim to evaluate the function fo relating variables u and y measured from a physical phenomenon. An exact knowledge of fo cannot be expected, and many methods are used for deriving approximate functions fa giving small error f
Externí odkaz:
http://arxiv.org/abs/2407.06225
In this paper, we propose a unified framework for identifying interpretable nonlinear dynamical models that preserve physical properties. The proposed approach integrates physical principles with black-box basis functions to compensate for unmodeled
Externí odkaz:
http://arxiv.org/abs/2405.18186
Autor:
Pagone, Michele, Novara, Carlo
This document briefly describes the noise models and shapes used for the synthesis of the Drag-Free and Attitude Control System in the LISA space mission. LISA (Laser Interferometer Space Antenna) is one of the next large-class missions from the Euro
Externí odkaz:
http://arxiv.org/abs/2405.02339
The aim of this paper is to present a novel physics-based framework for the identification of dynamical systems, in which the physical and structural insights are reflected directly into a backpropagation-based learning algorithm. The main result is
Externí odkaz:
http://arxiv.org/abs/2310.20567
We propose a Nonlinear Model Predictive Control approach to spacecraft rendezvous in non-Keplerian Lunar orbits. The approach is based on the Pontryagin Minimum Principle and allows the accomplishment of minimum-propellant maneuvers. The relative mot
Externí odkaz:
http://arxiv.org/abs/2309.05453
Hardening cyber physical assets is both crucial and labor-intensive. Recently, Machine Learning (ML) in general and Reinforcement Learning RL) more specifically has shown great promise to automate tasks that otherwise would require significant human
Externí odkaz:
http://arxiv.org/abs/2304.11052
Autor:
Hengster, Yasmin, Lellep, Martin, Weigel, Julian, Bross, Matthew, Bosbach, Johannes, Schanz, Daniel, Schröder, Andreas, Huhn, Florian, Novara, Matteo, Paz, Daniel Garaboa, Kähler, Christian J., Linkmann, Moritz
Using curvature and torsion to describe Lagrangian trajectories gives a full description of these as well as an insight into small and large time scales as temporal derivatives up to order 3 are involved. One might expect that the statistics of these
Externí odkaz:
http://arxiv.org/abs/2302.01844
Autor:
Novara, Chiara, Montesi, Daniel, Bertone, Sofia, Paccotti, Niccolò, Geobaldo, Francesco, Channab, Marwan, Angelini, Angelo, Rivolo, Paola, Giorgis, Fabrizio, Chiadò, Alessandro
Accurate design of labelled oligo probes for the detection of miRNA biomarkers by Surface Enhanced Raman Scattering (SERS) may improve the exploitation of the plasmonic enhancement. This work, thus, critically investigates the role of probe labelling
Externí odkaz:
http://arxiv.org/abs/2301.07062